Is freedom better? That's the question this project tries to answer using data. We examine the statistical relationship between economic freedom and quality-of-life indicators across 150+ countries.
Economic freedom, as measured by the Heritage Foundation's Index of Economic Freedom, encompasses factors like property rights, government integrity, tax burden, business freedom, and trade freedom.
Correlation does not imply causation — this is a fundamental principle of statistics. However, when studying complex societal phenomena like the relationship between economic systems and quality of life, establishing direct causation is practically impossible.
Unlike laboratory experiments where we can isolate variables and test hypotheses in controlled conditions, we cannot simply "remove" geography, history, culture, natural resources, or political systems from the equation to see how economies would behave differently. We cannot run randomized controlled trials on entire nations.
This means that correlation analysis, while imperfect, represents the best available evidence we can gather on this subject. The consistent, statistically significant correlations across multiple independent metrics and data sources provide a compelling picture — not proof, but the strongest empirical foundation currently available.
Until someone develops a better methodology to study these complex systems, this data-driven approach offers the clearest window into how economic freedom relates to human flourishing.
Data is gathered from reputable international organizations including the Heritage Foundation, World Bank, UN, Transparency International, and others.
Countries are matched across datasets by name. Only countries with valid data in both the Economic Freedom Index and the comparison metric are included in each correlation.
Pearson correlation coefficients are calculated to measure the linear relationship between economic freedom and each quality-of-life indicator.
P-values are calculated to determine if correlations are statistically significant (p < 0.05), meaning they're unlikely to occur by chance.
A correlation coefficient (r) measures the strength and direction of a linear relationship between two variables:
Context matters: In social sciences, 0.5 is considered a large effect size (Cohen, 1988) — roughly 75% of published correlations fall below that threshold.
For metrics where lower is better (like infant mortality), the sign is flipped in the "effective correlation" to make all positive correlations represent better outcomes.
Found a bug? Have a suggestion? Think we got something wrong? We'd love to hear from you. All feedback helps make this analysis better.